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咳嗽与上呼吸道日常活动产生的其他声音的区别。

Distinction of cough from other sounds produced by daily activities in the upper airways.

作者信息

Martinek J, Zatko T, Tatar M, Javorka M

机构信息

Department of Pathophysiology, Comenius University, Jessenius Faculty of Medicine, Martin, Slovakia.

出版信息

Bratisl Lek Listy. 2011;112(3):120-4.

Abstract

OBJECTIVES

The aim of this study was to validate the successfulness of our developed system for distinction between cough and other sounds which are present in daily human activities from the upper airways.

BACKGROUND

To date, methods used for monitoring of cough sound were primarily subjective. A reliable measure of cough is needed so that the severity of cough in various patients and the effectiveness of treatment can be assessed.

METHODS

Sounds of induced cough and sneezing, voluntary throat and nasopharynx clearing, forced ventilation and laughing, snoring, eructation, loud swallowing, and nasal blowing were studied. Characteristics of the sound events in 20 volunteers were calculated using the time-domain, spectral and non-linear analysis. The classification tree was constructed for classification between cough and non-cough sounds. We have validated the usefulness of our developed algorithm against subjective cough counts, which were performed by two trained observers.

RESULTS

The value of sensitivity for distinction between cough and other sounds was 86% and the value of specificity was 91%. The value of sensitivity for distinction between voluntary and induced cough sounds was 96% and specificity was 43%. The value of sensitivity between cough sounds and voluntary throat clearing was 96% and specificity was 85%. The value of sensitivity between cough sounds and induced sneezing was 95% and specificity was 93%.

CONCLUSION

We have developed an algorithm for distinction between cough and other sounds with a relatively high degree of accuracy (Tab. 1, Fig. 5, Ref. 15).

摘要

目的

本研究旨在验证我们所开发的系统能否成功区分咳嗽声与日常人类上呼吸道活动中出现的其他声音。

背景

迄今为止,用于监测咳嗽声的方法主要是主观的。需要一种可靠的咳嗽测量方法,以便能够评估不同患者咳嗽的严重程度以及治疗效果。

方法

研究了诱发咳嗽和打喷嚏的声音、自主清嗓和鼻咽的声音、用力呼吸和大笑的声音、打鼾声、嗳气声、大声吞咽声和擤鼻声。使用时域、频谱和非线性分析计算了20名志愿者声音事件的特征。构建了分类树以区分咳嗽声和非咳嗽声。我们已针对由两名经过培训的观察者进行的主观咳嗽计数,验证了我们所开发算法的有效性。

结果

区分咳嗽声与其他声音的灵敏度值为86%,特异度值为91%。区分自主咳嗽声和诱发咳嗽声的灵敏度值为96%,特异度值为43%。咳嗽声与自主清嗓之间的灵敏度值为96%,特异度值为85%。咳嗽声与诱发打喷嚏之间的灵敏度值为95%,特异度值为93%。

结论

我们已开发出一种区分咳嗽声与其他声音的算法,其具有相对较高的准确度(表1,图5,参考文献15)。

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